Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

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Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race

10.1101/2020.10.04.20206763 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xiao Hou ◽

Song Gao ◽

Qin Li ◽

Yuhao Kang ◽

Nan Chen ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Human Mobility ◽

Large City ◽

Machine Learning Techniques ◽

Epidemic Modeling ◽

Strong Response ◽

Modeling Framework ◽

Social Challenges ◽

Novel Coronavirus ◽

Global Threat

ABSTRACTThe novel coronavirus disease (COVID-19) pandemic is a global threat presenting health, economic and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health and shaping policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intra-county environments creates spatial heterogeneity of transmission in different sub-regions. To address this issue, we develop a new human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behavior. This new modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and human mobility, business foot-traffic, race & ethnicity, and age-group are then investigated. The results reveal that in a college town (Dane County) the most important heterogeneity is spatial, while in a large city area (Milwaukee County) ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate on various reopening policies, which suggests that policymakers may need to take these heterogeneities into account very carefully when designing policies for mitigating the spread of COVID-19 and reopening.

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STUDY OF SINGLE-SPECIES POPULATION MODELS

Towards Excellence ◽

10.37867/te120316 ◽

2020 ◽

pp. 161-166

Author(s):

Marthak Rutu

Keyword(s):

Spatial Heterogeneity ◽

Environmental Effects ◽

Age Structure ◽

Single Species ◽

Research Paper ◽

Population Models ◽

Deterministic Models ◽

One Dimensional ◽

Species Population ◽

Single Species Population

In this research paper one dimensional population models developed centuries ago shows that growth and/decay of single homogeneous populations But environmental effects spatial heterogeneity or age-structure deterministic models prevailing single species population models.

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Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17041381 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1381 ◽

Cited By ~ 2

Author(s):

Junyi Lu ◽

Sebastian Meyer

Keyword(s):

The United States ◽

Conditional Density ◽

Multiple Models ◽

Epidemic Modeling ◽

Short Term ◽

Modeling Framework ◽

Seasonal Peak ◽

Peak Intensity ◽

Probabilistic Forecasts ◽

Overall Performance

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.

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Sex, Race, and Place

Journal of Research in Crime and Delinquency ◽

10.1177/0022427818770790 ◽

2018 ◽

Vol 55 (4) ◽

pp. 493-537

Author(s):

Lyndsay N. Boggess ◽

Ráchael A. Powers ◽

Alyssa W. Chamberlain

Keyword(s):

Race And Ethnicity ◽

Criminal Behavior ◽

Structural Equation ◽

Negative Binomial ◽

Equation Modeling ◽

Structural Factors ◽

Neighborhood Structure ◽

Modeling Framework ◽

Male And Female ◽

Race Ethnicity

Objectives: We draw upon theories of social disorganization, strain, and subculture of violence to examine how sex and race/ethnicity intersect to inform nonlethal violent offending at the macrolevel. Methods: Using neighborhood-level incidents, we examine (1) the structural correlates of male and female nonlethal violence and (2) whether ecological conditions have variable impacts on the prevalence of White, Black, and Latino male and female offenses above and beyond differential exposure to disadvantage. We use multivariate negative binomial regression within a structural equation modeling framework which allows for the examination of the same set of indicator variables on more than one dependent variable simultaneously while accounting for covariance between the dependent variables. Results: We find few significant differences in the salience of disadvantage on female and male violence across race and ethnicity although some differences emerge for White men and women. Structural factors are largely sex invariant within race and ethnicity. Conclusions: Despite expectations that disadvantage would have differential effects across sex and race/ethnicity, we uncover only minor differences. This suggests that structural effects are more invariant than variant across subgroups and highlights the importance of investigating both similarities and differences when examining neighborhood structure, intersectionality, and criminal behavior.

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COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3481617 ◽

2022 ◽

Vol 13 (2) ◽

pp. 1-23

Author(s):

Han Bao ◽

Xun Zhou ◽

Yiqun Xie ◽

Yingxue Zhang ◽

Yanhua Li

Keyword(s):

Spatial Heterogeneity ◽

Real World ◽

Large Scale ◽

Census Data ◽

Human Mobility ◽

Training Data ◽

Estimation Accuracy ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

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Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

10.1101/462150 ◽

2018 ◽

Author(s):

Ying Zhang ◽

Jefferson Riera ◽

Kayla Ostrow ◽

Sauleh Siddiqui ◽

Harendra de Silva ◽

...

Keyword(s):

Land Use ◽

Sri Lanka ◽

Vector Control ◽

Urban Areas ◽

Temporal Dynamics ◽

Human Mobility ◽

Relative Role ◽

Land Use Patterns ◽

Modeling Framework ◽

Dengue Outbreak

AbstractBackgroundMore than 80,000 dengue cases including 215 deaths were reported nationally in less than seven months between 2016-2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission.MethodsWe present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km × 1 km spatial resolution and a weekly temporal resolution.ResultsOur results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions.ConclusionsOur study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

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A novel sub-epidemic modeling framework for short-term forecasting epidemic waves

BMC Medicine ◽

10.1186/s12916-019-1406-6 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 33

Author(s):

Gerardo Chowell ◽

Amna Tariq ◽

James M. Hyman

Keyword(s):

Epidemic Modeling ◽

Short Term ◽

Modeling Framework ◽

Short Term Forecasting

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SOLSTICE: Space Weather Modeling Meets Machine Learning

10.5194/egusphere-egu2020-5224 ◽

2020 ◽

Author(s):

Tamas Gombosi ◽

Keyword(s):

Machine Learning ◽

Space Weather ◽

High Performance ◽

Numerical Models ◽

Machine Learning Techniques ◽

Grand Challenge ◽

Science Data ◽

Modeling Framework ◽

Weather Modeling ◽

Solar Storms

The last decade has truly witnessed the rise of the machine age. The enormous expansion of technology that can generate and&#160;manipulate massive amounts of information has transformed all aspects of society. Missions such as SDO and MMS, and numerical models such as the Space Weather Modeling Framework (SWMF) are now routinely generating terabytes of science data, far beyond what can be analyzed directly by humans. Fortunately, concurrent with this explosion in information has come the development of powerful capabilities, such as machine learning (ML) and artificial intelligence (AI), that can retrieve revolutionary new understanding and utility from the massive data sets.&#160;SOLSTICE (Solar Storms and Terrestrial Impacts Center) is a recently selected NASA/NSF DRIVE Center. It&#160;will serve as the vanguard for&#160;developing and applying ML methods, which will then raise the capabilities of the entire community. We will combine&#160;next generation ML technology with our world-leading numerical models and the exquisite data from the space missions to make&#160;breakthrough advances in Heliophysics understanding and space weather capabilities, and then transition our technology to the&#160;CCMC for the benefit of all.We use ML to attack Grand Challenge Problems that cover the major aspects of space weather science:&#160;(i) use interpretable deep learning models, archived solar observations and high-performance physics-based simulations to identify the&#160;onset mechanism of solar flares and coronal mass ejections; and&#160;(ii) use high-cadence observations and physics-based feature learning to predict solar storms many hours before eruption, training&#160;time-to-event models to predict event times and flare magnitudes using innovative machine learning techniques.

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